The present inventive concept herein relates to image recognition devices, and more particularly, to an image recognition device reducing an arithmetic operation for image recognition and a method of recognizing an image thereof.
An image recognition device recognizes an object of things, animals, person, etc. in an image. An image recognition device determines boundaries for distinguishing an object in an image. An image recognition device uses a support vector to set a boundary line for recognizing an object. The support vector sets a location between an object for recognition and a boundary line.
The number of the support vectors and the order of the support vector have great values to classify an image being input, that is, an input vector. As an illustration, a support vector machine uses a nonlinear kernel. Thus, to increase performance of the support vector machine, a lot of support vectors are stored and an internal operation or a euclidean distance operation is performed. In a support vector used to recognize an image, the order N of each support vector reaches several thousands and the number M of support vectors is several hundreds through several thousands. To analyze one image being input into the support vector machine, a multiplying operation of N×N is needed. Thus, millions of registers and multiplying operations are needed. Furthermore, since a lot of hardware is needed for a real time processing, it is difficult to realize them.
A decision function of radial basis function-support vector machine (RBF-SVM) is represented by a mathematical formula 1 below
                              f          ⁡                      (            x            )                          =                  sign          ⁡                      (                                                            ∑                                      i                    =                    1                                    M                                ⁢                                                      α                    i                                    ⁢                                      y                    i                                    ⁢                                      ⅇ                                                                  -                        γ                                            ⁢                                                                                                                            X                                                          s                              ,                              k                                                                                -                          X                                                                                                                                                        +              b                        )                                              [                  mathematical          ⁢                                          ⁢          formula                ]            
Here, αi, yi, b, xs,i and x represent a weighted value, class 1 or −1, a bias, ith support vector and an input vector to be classified respectively. In the mathematical formula 1, an image recognition device uses a method of reducing a structure of support vector. Using that method, M may be reduced. Although a principal component analysis (PCA) method may be applied to reduce N, a memory for storing a projection operator obtained through a principal component analysis and the number of multiplication of memory needed for a projection are very large, it is difficult to be applied when an order N is great.
Increases of the number and the order of support vector increase an operation for object recognition.